The broadcasting of radio by means of the Internet is a fairly recent development and becomes more and more popular among users. Typically, as a listener logs onto a particular web site, audio files, usually songs, are played. The selections of which audio files are to be played are controlled by the owner or operator of the web site. In the past, in conjunction with conventional radio a desirable procedure was to get the listeners involved in conjunction with the broadcasting station. Generally, this type of procedure increased the number of listeners, and of course the greater number of listeners the more successful a radio station. One way to get the listeners involved is to have the radio station accept requests for particular songs or to hear certain audio files.
The art is replete with numerous prior art Internet based radio systems and methods. With the ever-growing popularity of acquiring music, a variety of these prior art consumer devices such as a digital media player (DMP) or a digital audio player (DAP) are used to play and manage digital music files, wherein these consumer devices may be a single functional device, a multifunctional device, such as a mobile phone, a personal digital assistant (PDA), or a handheld computer. Since these types of prior art consumer devices continually become more portable and versatile, our reliance on such devices for entertainment purposes has grown. In some instances, a user may create a playlist. The playlist may include one or more songs selected by the user that may be played, for example, in sequence or in random order. However, the process of creating a playlist can be time-consuming and burdensome.
There are numerous systems and methods in the prior art that allow the users of these aforementioned prior art devices to download use a principal component analysis (PCA) in order to determine likeness of certain music type of the users. The PCA is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has as high a variance as possible (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it be orthogonal to (uncorrelated with) the preceding components. Principal components are guaranteed to be independent only if the data set is jointly normally distributed.
The PCA is mostly used as a tool in exploratory data analysis and for making predictive models. The PCA can be done by eigenvalue decomposition of a data covariance matrix or singular value decomposition of a data matrix, usually after mean centering the data for each attribute. The results of the PCA are usually discussed in terms of component scores (the transformed variable values corresponding to a particular case in the data) and loadings (the weight by which each standardized original variable should be multiplied to get the component score). The PCA is the simplest of the true eigenvector-based multivariate analyses. If a multivariate dataset is visualised as a set of coordinates in a high-dimensional data space (1 axis per variable), the PCA can supply the user with a lower-dimensional picture, a “shadow” of this object when viewed from its (in some sense) most informative viewpoint. This is done by using only the first few principal components so that the dimensionality of the transformed data is reduced.
Numerous prior art references use the PCA approach in its systems and methods. One of such prior art references in United States Patent Application Publication No. 20090116684 to Andreasson (the Andreasson reference). The Andreasson reference teaches a system and method for generating a playlist of songs based on facial expression of a user. The method includes playing a first song on a device and capturing an image of a user, performing facial expression recognition of the user based on the image, and selecting a second song based on a facial expression of the user. The method taught by the Andreasson reference fails to solve the aforementioned problems because the process of creating a playlist of songs based on this method will be time-consuming and burdensome simply because different users have different personalities and not every user will show facial expressions in response to the song played or image presented to the user. Some users may still have facial expressions that will not be captured by the system of the Andreasson reference. Another problem of the system taught by the Andreasson reference is inaccuracy of the facial expression determination because some users may present such facial expression that can be visible to the system as if the user is unhappy with the image presented to the user wherein, in fact, the user likes the image.
Another prior art reference, namely United States Patent Application Publication No. 20080021851 to Alcade et al. (the Alcade reference) teaches system uses the PCA approach, wherein a series of complex artificial intelligence algorithms analyze a plurality of sonic characteristics in a musical composition, and is then able to sort any collection of digital music based on any combination of similar characteristics. The characteristics analyzed are those that produce the strongest reaction in terms of human perception, such as melody, tempo, rhythm, and range, and how these characteristics change over time. This approach enables the creation of “constellations” of music with similar characteristics, even from different genres and styles, enabling fast yet highly individualized music discovery. Further personalized music discovery is enabled based on a “Music Taste Test”.
To provide users with music recommendations, the system employs a number of analysis functions. A “Music Taste Test” (MI Mood module) function learns a user's music preferences via a series of binary choice questions, and delivers lists and/or personalized song recommendations to the user based on this information. Recommendations are prioritized and listed in order of closest song match on a theoretical multi-dimensional grid. A “Soundalikes” function links songs having similar musical/mathematical profiles enabling for music recommendation. A “Discovery” function that also links songs having similar mathematical patterns, but that allows for a wider recommendation than the “Soundalikes” function. The “Music Taste Test” function and “Soundalikes” function cooperate to establish ‘moods’ for each song, such as happy, sad, calm, and energetic.
To the extend effective and more advanced as compared with the system and method of the Andreasson reference, the system of the Alcade reference presents numerous drawbacks. For example, not every user will be willing to go through a plurality of questions in order to answer them to determine the type of music that the user will like. This procedure is time consuming and to some extent may not be practicable to those users who may not understand English or not understand the question.
Another prior art reference such as U.S. Pat. No. 4,839,853 to Deerwester et al. (the Deerwester reference) teaches a method of latent semantic analysis (the LSA), which is completely different from the PCA approach. This method presents a technique in natural language processing, in particular in vectorial semantics, wherein the method analyzes relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. As further taught by the Deerwester reference, the LSA assumes that words that are close in meaning will occur close together in text. A matrix containing word counts per paragraph (rows are represented by unique words and columns are represented by each paragraph) is constructed from a large piece of text and a mathematical technique called singular value decomposition (the SVD) is used to reduce the number of columns while preserving the similarity structure among rows. Words are then compared by taking the cosine of any two rows. Values close to 1 represent very similar words while values close to 0 represent very dissimilar words.
To the extend effective, the LSA application as disclosed in the Deerwester reference fails to teach application that will allow to determine preference of the users to certain type of music thereby clusterizing the users into groups in order to provide the users with music of their preference.
Therefore, an opportunity exists for an improved system and method whereby users will enjoy playlist of songs based on the user's choice and preference will be presented to the users based on initial questionnaire wherein the users will not select songs to create the playlist thereby eliminating the need for creation of such playlist that is time-consuming and burdensome.